Inference improvement by enlarging the training set while learning DFAs

Authors:
Pedro García;José Ruiz;Antonio Cano;Gloria Alvarez
Affiliations:
Departamento de Sistemas Informáticos y Computación, Universidad Politécnica de Valencia, Valencia, Spain;Departamento de Sistemas Informáticos y Computación, Universidad Politécnica de Valencia, Valencia, Spain;Departamento de Sistemas Informáticos y Computación, Universidad Politécnica de Valencia, Valencia, Spain;Seccional Cali, Grupo de Investigación DESTINO, Pontificia Universidad Javeriana, Cali, Colombia
Venue:
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
Year:
2005

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Abstract

A new version of the RPNI algorithm, called RPNI2, is presented. The main difference between them is the capability of the new one to extend the training set during the inference process. The effect of this new feature is specially notorious in the inference of languages generated from regular expressions and Non-deterministic Finite Automata (NFA). A first experimental comparison is done between RPNI2 and DeLeTe2, other algorithm that behaves well with the same sort of training data.